This paper investigates the role of socioeconomic considerations in the formation of official COVID-19 reports. To this end, we employ a dataset that contains 1159 pre-processed indicators from the World Bank Group GovData360 and TCdata360 platforms and an additional 8 COVID-19 variables generated based on reports from 138 countries. During the analysis, a rank-correlation-based complex method is used to identify the time- and space-varying relations between pandemic variables and the main topics of World Bank Group platforms. The results not only draw attention to the importance of factors such as air traffic, tourism, and corruption in report formation but also support further discipline-specific research by mapping and monitoring a wide range of such relationships. To this end, a source code written in R language is attached that allows for the customization of the analysis and provides up-to-date results.
Predictive maintenance is a powerful maintenance strategy that makes it possible to significantly reduce operation and maintenance costs of public, commercial and industrial environments. It is a complex data-driven process, which tries to forecast future states of company assets. On one hand it prerequisites condition monitoring of components on machine level. On the other hand it demands the integration of the collected data with other management information systems. Digitization and especially the advent of big data science bring along promising opportunities to create effective smart monitoring and predictive maintenance applications. The aim of this research is to examine the possibilities of a predictive maintenance framework based on the design principles of Industry 4.0 and recent developments in distributed computing, Big Data and Machine Learning. It introduces numerous enabling technologies such as industrial Internet of things, standardized communication protocols, as well as edge and cloud computing. Moreover, it takes a deeper look at data analytical techniques and tools, and analyses performance of well-known machine learning algorithms. Paper proposes architecture of a predictive maintenance framework based on existing software and hardware solutions. As a proof of concept, a real-life smart heating, ventilation, and air conditioning (HVAC) application system is created and tested to demonstrate the possibilities of the proposed PdM framework.
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